Variable Coding, Recoding and Codebook Creation for Analysis-Ready Data

What this service covers

• Numeric coding of categorical answers and multiple-response questions
• Reverse-coding of negatively worded items
• Grouping continuous variables into meaningful categories
• Derived variables such as age groups, indices and wealth quintiles
• Scale and subscale scores with documented formulas
• Variable and value labels in SPSS, Stata, R or Excel
• A codebook describing each variable, its coding and its source question

Why coding decisions matter

How you code answers affects what analyses are possible. Collapsing categories too early loses detail; leaving them too fine can make tests unreliable. We keep the original variables, create new ones for analysis and note exactly how each was derived, so that others can reproduce the work.

Common tasks

Examples include turning “select all that apply” questions into separate indicator variables, recoding “don’t know” to missing, reverse-coding items before computing a Likert scale score, and building composite indices. We check every derived variable against the original data to catch coding mistakes.

The codebook

A codebook lists each variable name, label, type, allowed values, coding scheme and notes. It is vital for team projects, data sharing and long-term use. Qualitative projects need one too; see thematic analysis for coding frames of text.

Part of a larger workflow

Coding follows cleaning and validation and often comes with missing data treatment and merging. Good questionnaire design reduces coding work; see questionnaire design.

How the work runs

1. You send the dataset and questionnaire.
2. We agree coding rules and derived variables.
3. We apply them and verify against the raw data.
4. You receive the coded dataset and codebook.

What you receive

• Dataset with variable and value labels
• Derived variables and scale scores
• Codebook or data dictionary
• Syntax or script of all recodes
• Verification checks